Generative AI

Full definition

Generative AI is built on foundation models, most commonly transformers (text, code) and diffusion models (image, video, audio). The 2022–2024 wave brought generative AI from research demos to mass adoption: ChatGPT reached an estimated 100 million monthly users in January 2023, two months after launch, a pace UBS analysts called unprecedented for a consumer app. Generative AI is distinct from "predictive" or "discriminative" AI, which classifies inputs into categories rather than producing new ones.

Why it matters

Generative AI is the most economically significant AI shift in a decade. Goldman Sachs estimated in 2023 that generative AI could raise global GDP by 7% over a decade, and McKinsey put the annual productivity opportunity at $2.6–4.4 trillion. For executives, this is no longer pilot territory — it is mainline P&L.

Example

A marketing team uses generative AI to produce 200 localized ad variants in 9 languages overnight; the same team then uses generative AI to draft, score, and A/B-test landing-page copy.

Next steps

• Get a free workflow map →We map one of your workflows and give you a written verdict: software, a process change or AI. No obligation.
• AI consulting cost guide →Hourly rates and project budgets by firm tier, from independents to the Big Four.

Related terms

• Large Language Model (LLM)
  A Large Language Model (LLM) is a deep neural network — almost always a transformer — trained on hundreds of billions to trillions of words to predict the next token, and to generate, summarize, translate, or reason over text.
• Foundation Model
  A foundation model is a large model trained on broad data at scale — typically self-supervised — that can be adapted to many downstream tasks via prompting, fine-tuning, or retrieval, instead of being trained task-by-task.
• Diffusion Model
  A diffusion model is a generative model that learns to reverse a gradual noising process — starting from random noise and iteratively denoising it into a coherent image, audio waveform, or video — and is the dominant architecture behind modern AI image and video generation.
• Multimodal Model
  A multimodal model is an AI model that natively understands or generates more than one type of input — typically text plus images, audio, or video — within a single network rather than via separate task-specific models.
• Hallucination
  An AI hallucination is when a language or generative model produces content that is plausible-sounding but factually incorrect, fabricated, or unfaithful to its sources — one of the biggest failure modes of LLMs in regulated and high-stakes domains.

More models terms

• Transformer
  The transformer is a neural-network architecture built around the self-attention mechanism that has become the dominant model design for language, vision, audio, and multimodal AI since 2017.